dmux-workflows

You run parallel AI agent sessions by launching a dmux session to create and manage multiple panes across harnesses like Claude Code, Codex, and OpenCode. This enables concurrent experimentation and collaboration across different agent environments.

Multi-agent orchestration with dmux coordinates parallel AI agent sessions using tmux panes. It manages concurrent tasks across multiple harnesses for research, development, testing, and cross-harness collaborations by creating, merging, and organizing agent panes.

Yes, dmux supports merging and organizing pane outputs into a single workflow. You can streamline results from concurrent agent runs across Claude Code, Codex, and OpenCode into one consolidated output for easier review and integration.

Dmux supports common workflow patterns including research+implement, multi-file feature development, test+fix cycles, cross-harness coordination, and code-review pipelines. These patterns leverage parallel pane-based orchestration across multiple agent harnesses.

The best way to coordinate cross-harness AI collaborations is using dmux to create parallel agent panes across Claude Code, Codex, and OpenCode. Dmux organizes these concurrent sessions into workflow patterns like code-review pipelines by managing and merging pane outputs.

Dmux relies on pane-based orchestration to manage parallel AI agent sessions, implying a tmux-like environment is needed for creating and organizing panes. The tool manages panes across harnesses like Claude Code and Codex for concurrent task execution.